Exploring Cloud Computing Adoption in Higher Educational Environment: An Extension of the Tpb Model With Trust, Peer Influences, Perceived Usefulness and Ease of Use

International Journal of Computer Science & Information Technology (IJCSIT) Vol 15, No 4, August 2023
DOI: 10.5121/ijcsit.2023.15406 67
EXPLORING CLOUD COMPUTING ADOPTION IN
HIGHER EDUCATIONAL ENVIRONMENT: AN
EXTENSION OF THE TPB MODEL WITH TRUST,
PEER INFLUENCES, PERCEIVED USEFULNESS AND
EASE OF USE
Waleed Al-Ghaith
Imam Mohammad Ibn Saud Islamic University (IMSIU)
ABSTRACT
Cloud computing is regarded as the next generation of computing. It is progressively being used as a
launching pad for digital innovation and organizational agility. Cloud computing is frequently used by
private and public organizations due to its flexibility, collaboration, cost-effectiveness, and scalability.
These characteristics make cloud computing indispensable for individuals and businesses such as higher
education institutes. Several prior studies covered the technological facets of cloud-based contexts,
including cloud security, scalability, and virtualization. However, it is contend that the main barrier to
cloud computing isn't technical but cognitive or behavioural, and in particular attitudinal. Thus, this
research aims to study higher education’ students’ attitudes and their intention to adopt cloud computing,
with a specific concentration on the effect of trust, peer influences, perceived usefulness and ease of use in
order to investigate the factors influencing the adoption of cloud computing in higher educational
environment in Saudi Arabia. This study presents an extended Decomposed Theory of Planned Behaviour
(DTPB) to include trust, peer influences, perceived usefulness and ease of use as a cognition, representing
a person’s perception of social influence to perform or not perform a behaviour under consideration. The
proposed model was able to explain 62% of the variance in behavioural intention and 65% of students'
attitudes towards the adoption of cloud computing in higher educational environment. The study's findings
show that the proposed model explained a significant amount of variation in cloud computing adoption. It
suggests that the model expansion by incorporating trust, peer influences, perceived usefulness and ease of
use factors were valuable explorations. Further, the findings demonstrate that university students' attitudes
toward using cloud computing are significantly influenced by perceived ease of use, trust in cloud
computing service provider and perceived usefulness, which have the ability to explain their attitude by
22.15%, 21.9% and 20.9% respectively. The result also shows that "subjective norm" alone explains
33.95% of students' "behavioural intentions" towards using cloud computing, followed by their "attitude,"
which explains around 14.24% of "behavioural intentions," and then university students’ "self-efficacy,"
with 13.71%..
KEYWORDS
Cloud Computing, DTPB, Trust, Technology Acceptance, Peer influences, Perceived usefulness, Perceived
ease of use.
1. INTRODUCTION
Emerging innovations in cloud computing have piqued the interest of IT professionals across the
world. Cloud computing is an advanced form of distributed networking that enables the sharing
of hardware and software assets among various public and private sectors and businesses [1]. The

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notion of "cloud computing" means the hardware and systems that are used to provide services,
as well as the applications that are created and distributed via the Internet [2].
Cloud computing is regarded as the next generation of computing. It is progressively being used
as a launching pad for digital innovation and organizational agility. Cloud computing is
frequently used by private and public firms due to its cost-effectiveness, scalability, collaboration
and flexibility. These characteristics make cloud computing indispensable for individuals and
businesses such as higher education institutes. Research and Markets [3] reported that the global
Cloud Computing in Higher Education market was worth more than us$ 2,182.4 million in 2020
and is expected to grow to USD 8,779.1 million by 2027. Cloud computing has emerged as a
crucial technology in the modern era, and it is now designated the fifth utility following the four
fundamental utilities, namely, electricity, water, gas, and telecommunication [4], [5].
Higher education institutions are experiencing difficulties with the recruitment of participants, the
demand for IT, the quality of education services, and affordability [6]; [7]; [5]. Thus, higher
education institutions work diligently to manage resources and offer better services [8]; [9].
Higher education institutions represented by educators and students have found the benefits
offered by cloud computing, such as cost reduction, educational sustainability, and raising
educational quality, an opportunity to achieve their goals [10]; [11]; [12]. The adoption of cloud
computing in higher education institutions is gaining ground, but it is still considered to lag
behind the commercial sector and even government organizations in this market [13]. Yet, it is
increasingly becoming a necessary element of higher education institutions' offerings rather than
an option, which has boosted competitiveness in the higher education market [14]. However,
despite its numerous benefits, cloud computing still faces significant barriers among university
students to adopt it. Many students are comfortable with traditional methods of storing data, such
as USB drives or hard disks. They may also be skeptical about relying on third-party providers
for their data storage needs.
A number of earlier studies covered the technological facets of cloud contexts, including cloud
scalability, virtualization, and security [15]; [16]. However, it is contend that the most significant
barrier to cloud computing is not technical but rather cognitive or behavioural, particularly
attitudinal [17]; [18]. Thus, this research aims to study university students’ students’ attitudes and
their intention to adopt cloud computing, in order to investigate the factors influencing the
adoption of cloud computing among higher education students in Saudi Arabia. This study
develops a conceptual model based on the Theory of Planned Behaviour (TPB) that lends itself to
investigating these factors. Moreover, this study extends TPB to investigate drivers of cloud
adoption among higher education students in Saudi Arabia by placing peer influences, perceived
usefulness, ease of use, and "trust" of cloud computing providers as new constructs within the
TPB model. A quantity of research has recognized and anticipated the importance of "trust" in
cloud computing providers, perceived ease of use, perceived usefulness and peer influences in
cloud computing settings, but merely scarce scholars have investigated the impact these factors
have on attitudes and behavioural intentions toward cloud computing usage. The study's objective
is to further comprehend higher education students' attitudes toward cloud computing.
This research will participate to the body of extant literature by elucidating the "trust" role in
cloud computing providers, perceived ease of use, perceived usefulness and peer influences in
cloud adoption behaviour. Additionally, it will validate whether the TPB is a reliable model
based on its ability to explain higher education students' attitudes and intents in the cloud
environment. This begs the following research question: what factors affect higher education
students’ attitudes and their intention to adopt cloud computing? The rest of this paper addresses
this question by presenting and extending the TPB as a potential theory for explaining differences
in adoption behaviour. This paper is organized as follows: the following section presents relevant

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earlier studies on cloud computing and the study's theoretical framework, which includes the TPB
as the main theory that guides the development of the study model. The third section discusses
the development of research hypotheses and the study model. The fourth section describes the
study methodology, including its measurements and applied data collection procedures. The fifth
section presents the research data analysis and its findings, which cover the reliability and
validity of the study instrument and the hypotheses testing findings. The next section provides a
discussion that includes the implications of the study findings for theory and research. Finally,
the last section presents conclusions.
2. PRIOR RESEARCH AND THEORETICAL FRAMEWORK
2.1. Cloud Computing and Higher Educational Environment
Cloud computing represents the convergence of two key trends in information technology: IT
efficiency and business agility. IT efficiency entails leveraging highly scalable hardware and
software resources to make better use of present computing capabilities. Business agility, on the
other hand, is the ability to use IT as a competitive tool through rapid development and mobile
interactive applications that respond instantly to user needs [19]; [17]. As a term, cloud
computing is defined variously in the literature. In this study, the definition of cloud computing
presented is compatible with NIST standards [20], which considers cloud computing as a
collection of characteristics shared by all cloud computing services. Consequently, in the context
of this paper, cloud computing refers to the applications and shared services used at the
institutions studied via subscription-based models, through which shared data servers or
application tasks can be made available. Cloud computing is continually altering the concepts of
learning and business. Accordingly, academics and the business sectors have adopted novel
technologies to remain contemporary with internal and external changes.
In higher education institutions, cloud computing has been considered mostly as a technical
advancement with transformational potential [21]. This is due to the fact that cloud computing
reaps the rewards of rapid IT implementation, particularly for research, which is considered more
favourably when compared with conventional software solutions. Furthermore, Cloud computing
technologies can be used as an aid in the implementation of socially oriented approaches to
learning as well as collaborative education [22]; [23]. Cloud computing resources can be utilized
to form e-learning platforms and educational services by centralizing data storage, virtualization,
and other services [24]. With these considerations in mind, cloud computing services are
becoming critical for various higher education institutions, and many of them rely on these
services to lower costs, maintain competitiveness, and meet student and educator needs [25].
Cloud computing services compose of several notions, can be utilized to improve students'
learning efficiency. According to Thomas [26], cloud computing allows students and educators to
engage with one another at any time from any location and collaborate on the same documents to
make modifications and improve the documents collectively. Many cloud-based applications
have the ability to enhance engagement and active learning among students, which subsequently
improves their performance [27]. For example, Checkpoints, Cloud, and Collaboration (C3) is a
learning framework that has been developed to improve learning outcomes. The C3 Framework
is designed to provide students with a structured approach to learning that incorporates the use of
technology and collaboration. The C3 Framework consists of three key components: checkpoints,
clouds, and collaboration. Checkpoints are used to monitor student progress and provide
feedback on their performance. The cloud component provides students with access to online
resources and tools that can be used for research, collaboration, and communication.
Collaboration is an essential component of the C3 framework as it encourages students to work
together on projects and assignments. The C3 framework has been shown to be effective in

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improving learning outcomes by providing students with a more engaging and interactive
learning experience. By incorporating technology into the learning process, students are able to
access a wider range of resources and collaborate more effectively with their peers.
This framework is based on the idea that learning should be a collaborative process where
students work together to achieve their goals. The C3 Framework is designed to provide students
with the tools they need to collaborate effectively, including cloud-based technology and regular
checkpoints. The C3 framework is particularly useful for online learning environments, where
collaboration can be challenging due to the lack of face-to-face interaction. By using cloud-based
technology, students can easily share information and work together on projects in real-time.
Regular checkpoints also help to keep students on track and ensure that they are making progress
towards their goals. Overall, the C3 framework provides a powerful tool for improving learning
outcomes for students. By promoting collaboration and providing students with the tools they
need to succeed, this framework can help create a more engaging and effective learning
environment for all learners [27].
Use of cloud services such as Classroom, G-mail, Google Docs, Dropbox, I-Cloud, and Sky
Drive can be easily integrated into educational environments [11], which may efficiently improve
educational institutions' ability to offer facilities or satisfy students' learning needs without
investing in costly hardware or software or needing to train students [28]. Adoption of cloud
computing has grown and become more pertinent to academic students and educators over the
past few years. In response, it has become a growing trend among scholars to use several
technology adoption models to comprehend how higher education students and educators adopted
cloud computing [29]. For example, a study conducted by Asadi et al. [8] used the Theory of
Planned Behavior (TPB) to investigate the determinants of cloud computing services among 240
faculty members in a medical university. The study found that attitude, perceived
privacy/security, perceived behavioral control, intention, and subjective norms factors altogether
explained about 59% of individuals’ behaviors toward adoption of cloud computing services [8].
Another study by Chiniah et al, [30] proposed a Hybrid model include Technology-Organization
Environment Model (TOE) and Technology Acceptance Model (TAM) to evaluate the previously
identified determinants for cloud adoption/non-adoption by the Mauritius ICT industry. The
study surveyed 93 ICT-related companies/organizations and found that security is no longer the
major concern for cloud adoption and companies are more focused on the advantages cloud
computing can offer to their operations [30].
The literature review found that numerous studies on adoption of cloud computing in academic
institutions had been published and that the pace of publication had been growing in recent years.
IS scholars have often studied cloud computing adoption in higher education institutions from the
perspective of individuals [11]; [13] or organizations [31]; [32]. Several theoretical and practical
contributions have been examined, many of which imply that variables affecting the adoption of
cloud computing in higher education institutions are numerous. This study contributes to this
accumulated effort by proposing and developing a theoretical model that lends itself to
investigating factors influencing cloud computing adoption among higher education students in
Saudi Arabia.
2.2. The Decomposed Theory of Planned Behaviour (DTPB)
A deconstructed form of the TPB that combines a number of TAM and DOI elements is known
as the Decomposed Theory of Planned Behaviour (DTPB). In comparison to the TAM and TPB
models, it exhibits a similar match to the pure TPB model with a little higher predictive power
[33]. The decomposed Theory of Planned Behaviour, according to Taylor and Todd, focuses on
the aspects that are likely to impact system use to offer a more complete knowledge of

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behavioural intention [33] and "enables application of the model to a variety of situations" [34]
because it makes the relationships between beliefs, attitudes, and intentions more transparent and
understandable. In the DTPB, attitudes, normative beliefs, and control beliefs are divided and
categorized according to the literature, the TAM and Diffusion of innovation theories. According
to theories put out by academics, normative belief may be separated into relevant groups of
references including subordinates, superiors, and peers each of whom has a unique viewpoint on
how IT should be used. Taylor and Todd [33] have so utilized the two groups of peers and
superiors to symbolize the breakdown of normative beliefs. In contrast, the control beliefs may be
split into two separate categories: self-efficacy and facilitating conditions. Self-efficacy refers to
one's perception of one's ability to use a new innovation, while the facilitating conditions group
offers two aspects for control beliefs: one relates to resource factors like time and money, and the
other concentrates on compatibility of technology difficulties which could constrain adoption
[35]; [34].
3. RESEARCH MODEL AND HYPOTHESES DEVELOPMENT
A modified version of the DTPB model was utilized in the current study to better understand the
factors influencing the decisions of higher education’ students to use cloud computing in Saudi
Arabia (see Figure 1). The next sub-sections provide a discussion of the model's structures as
well as the proposed hypotheses.
3.1. Trust and User Attitude
In any business transaction, trust is a crucial component, especially in technological settings
where there is uncertainty or insufficient product information [36]. Previous studies on the
adoption of cloud computing have not extensively studied trust as a multidimensional construct;
however, the majority of the literature supports the significance of a generalized trust construct as
a determinant in cloud computing adoption. To know the importance of trust in the use of cloud
computing, let's take a look at one of the aspects that requires trust and that affects the user's
attitude towards it.
Within the cloud computing context, the trusting intentions were subsequently influenced by the
trusting beliefs regarding cloud service providers, which predicted cloud adoption and success
factors [37]. When analysing trust in the cloud provider, trust includes all related expectations,
such as the conviction that the provider won't act opportunistically [37]. Cloud providers can
create adverse circumstances for organizations or individuals using their services, and moving
from one cloud provider to another can be costly and resource-intensive. Cloud providers may
use standards, closed architectures, proprietary software or complex licensing schemes to keep
customers captive [38]. It's also possible that a particular cloud provider will decide to disregard
agreements, rules, or guarantees, or will otherwise falsify compliance, or will exploit the client
organization in circumstances that aren't covered by the licensing agreement [37]. Thus, the
relationship between perceived trust in cloud service provider and individuals’ attitude toward the
cloud technology adoption, have been shown to be significant and the key antecedents of
behavioural intention to adopt the cloud [39]. Considering the above; the following hypothesis is
formulated:
H1. The trust in cloud service providers has a significant and positive influence on higher
education students ' attitudes toward the cloud.

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3.2. Perceived usefulness, perceived ease of use and user attitude
Perceived usefulness is a concept that has been widely studied in the field of psychology and
information systems. It refers to the degree to which an individual believes that a particular
product or service will be beneficial in achieving their goals or solving their problems [40].
Scholars investigated whether users are driven to embrace a new technology due to its usefulness
or fun. According to the findings of this study of [41], perceived usefulness is more influential
than perceived fun in deciding whether to accept or reject computer technology [41]. In
educational environment, perceived usefulness or relative advantages, and ease of use have been
demonstrated in many prior studies to be antecedent drivers or factors that have positive
influence on adoption of technology [42]; [43].
The perceived usefulness of using cloud computing for university students includes increased
accessibility, flexibility, collaboration, and cost-effectiveness. One of the significant advantages
of using cloud computing for university students is the ability to access course materials from
anywhere at any time. With cloud-based storage solutions like Google Drive or Dropbox,
students can easily upload and download lecture notes, assignments, and other study materials
from their laptops or mobile devices. This flexibility allows them to study on-the-go without
being tied down to a specific location. Another benefit of cloud computing for university students
is collaboration with peers and professors. Cloud-based platforms like Google Docs allow
multiple users to work on the same document simultaneously, making group projects more
manageable and efficient. Additionally, professors can share class notes or assignments with their
students through cloud-based learning management systems like Blackboard or Canvas. Another
of the main benefits of cloud computing is its simplicity. Students do not need to worry about
installing software or managing hardware, as everything is hosted in the cloud. This makes it
easier for them to focus on their studies without having to deal with technical issues. Another
advantage of cloud computing is its scalability. As students' needs change over time, they can
easily scale up or down their usage of cloud services without having to invest in additional
hardware or software licenses. Overall, the perceived ease of using cloud computing has made it
a popular choice among university students. Its simplicity and scalability make it a convenient
option for those who need to access their files and applications from anywhere at any time.
TAM that were proposed by Davis [40] to identify factors that influence users’ acceptance of new
technologies [44] proposes that two constructs (perceived ease of use and perceived usefulness)
form the behavioral beliefs to be predictors of an individual's attitude to IT, which in turn
determines their adoption of information technology. Within the main fundamentals of the TAM,
it hypothesized that user acceptance of information technology is determined by his or her
behavioral intention to use the IT, which can be predicted by his/her attitude towards using IT
and his/her perception about the usefulness related to use. Thus, the following four hypotheses
were occupied from the original TAM; however, they were adjusted for the existing study in
order to be appropriate in this context.
H2: Perceived usefulness of the cloud has a significant and positive influence on attitudes toward
the cloud.
H3: Perceived ease of use has a significant and positive influence on attitudes toward the cloud.
H4: Attitudes toward the cloud has a significant and positive influence on behavioral intention to
use the cloud.
H5: Behavioral intention has a significant and positive influence on the actual use of the cloud.

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3.3. Subjective Norm, Peer influences and user behavioural intention
An individual's perception of pressure from society from relevant referents to engage in or refrain
from doing something is known as the subjective norm. [45]; [46]. In other terms, people are
often interested in activities or objects if there is a positive attitude regarding them and think that
influential persons believe they must do this [46]. In this case, the behaviour is using cloud
technology for academic purposes; university students are heavily influenced by their peers when
it comes to adopting new technologies. If their peers are using cloud technology for academic
purposes, they are more likely to do so as well. This is because they feel that it is socially
acceptable and expected of them.
It refers to the perceived pressure from peers to use cloud-based services. Students tend to follow
what their friends are doing, and if their peers are using cloud-based services, they are likely to
do so as well. This is because students want to fit in with their social group and be seen as tech-
savvy. The group theory influence states that individuals often conform to the expectations of
others in order to deepen associations with them or, in some circumstances, to evade punishment
[47]; [48]. A student could think, for instance, that the teacher recommends using the e-learning
system. A favourable effect on the subjective norm may happen if the student is very motivated
to adhere to the teacher's expectations [48]. This further confirms the impact of subjective norm
on intentional behaviour. This lends credence the effect of subjective norm on intentional
behaviour. The theories of TPB [46] and TRA [45] use subjective norm to measure social effect
on intentional behaviour. Consequently, numerous earlier research [8]; [49]; [50] proposed a
positive correlation between behavioural intentions and subjective norms. Thus, we hypothesize
the following:
H6. The higher education students’ peer influences has a significant and positive influence on
their subjective norm toward the cloud.
H7. The higher education students’ subjective norm has a significant and positive influence on
their intention to adopt the cloud.
3.4. Self-Efficacy & Behavioural Intention
Self-efficacy refers the perception of a person’s ability to use new innovation. Self-efficacy is
related to a person's belief in his or her capacity to do a certain activity within a specified field
[51]; [48]. Self-efficacy is defined by Bandura [52] as "beliefs in one's capabilities to organize
and execute the courses of action required to produce given attainments" [52]. The association
among decisions regarding the usage of new technology and computer self-efficacy was
supported by additional research [53]; [51]; [44]; [54]; [55]; [56].
In the context of cloud usage in education, university students who have a greater level of self-
efficacy have a significant anticipation of being able use cloud computing successfully without
relying on ongoing help, and as a consequence, they find cloud computing advantageous. As a
result, they are more willing than others to adopt cloud computing [57]; [58]. Accordingly, we
propose that the self-efficacy construct indirectly influences on adoption behaviour with its
immediate impact on behavioural intention.
H8. The higher education students’ self-efficacy has a significant and positive impact on their
intention to adopt the cloud.

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Figure 1. The study model
4. RESEARCH METHODOLOGY
4.1. Measurement
Defining the concepts or constructs that a researcher intends to test, and then selecting proper
measuring methods to assess those constructs, is critical and has a substantial impact on the
accuracy of findings [1]. In this research, the survey instrument was created by the researcher to
test the study hypotheses. Items from prior studies were selected and included in the survey
questionnaire to evaluate the constructs, which ensuring the scale's face (content) validity. The
items have extensively employed in the majority of previous studies, which suggests that there is
probably researchers' subjective acceptance that these measuring instruments seem to accurately
represent the constructs of interest. In Table 1, the items created in this research for every
construct are provided along with the prior studies from which they were adapted.
4.2. Data Collection Procedures
The purpose of this research is to identify the factors effecting adoption of the cloud computing
phenomenon among higher education students in Saudi Arabia. To achieve the study goals, the
study's sample surveyed students of Shaqra and Imam Mohammad Ibn Saud Islamic universities.
A fully completed survey was obtained from 386 students. After checking the data for validity,
364 of them were deemed fit for use. In information systems research, an adequate sample size
for undertaking partial least squares (PLS) path analysis is critical [60]. A typical information
systems study would have at least 0.25 R-squared values, a 5% significance level, and 80%
statistical power. A sample size of 59 is thought to be adequate when using such attributes with a
maximum of three arrows pointing to a latent variable [61] as defined in the study's structural
equation model (see Figure 1). However, with the aforementioned parameters and factor loadings
of 0.5, the ideal sample size is 78 [60]. As a result, the sample size of 364 seemed to be more than
adequate for this study.

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5. DATA ANALYSIS AND RESULTS
5.1. Reliability and Validity
The instrument's internal consistency and reliability have been tested using the collected data
from the pilot study of every component in the instrument. The findings indicate that alpha values
ranged from .912 to .997, with a mean of .955 (see Table 2). This implies that each construct in
the model was reliable. The internal consistency was therefore adequate.
Table 1. List of items by construct
Construct Items Adapted from
Peer Influences
(PI)
My friends or my classmates would think that I should use
Cloud computing. (It have been used in the first and second
items to measure the Subjective Norm construct so will not be
used again)
[2]
Self-Efficacy
(SE)
I can use Cloud computing even if there was no one around to
show me how to do it
I can use Cloud computing with merely the online help
function as a guide.
I could easily utilize any of the Cloud computing websites on
my own if I wanted to.
Regardless of whether I had never used a system like it before,
I would be able to use Cloud computing.
[2]
[3]
Subjective
Norm
(SN)
My friends would think that I should use the Cloud computing
to achieve my needs
My colleagues/classmates would think that I should use the
Cloud computing to achieve my needs
People who are important to me would think that I should use
the Cloud computing to achieve my needs
[2],
[3].
Perceived
usefulness
(PU)
Cloud computing is more convenient than other traditional
options.
Cloud computing makes it easier to do my work
Cloud computing improves my work
Cloud computing help me to do my work more quickly
I think that Cloud computing is useful.
Overall, I think that using the Cloud computing is
advantageous.
[4],
[2],
[5].
Perceived
Ease of Use
(EU)
Learning to use cloud computing was easy for me
I find cloud computing easy to use
When I use cloud computing, the English language is not an
obstacle.
[4],
[2],
[5].
Attitude
(AT)
I have positive opinion in cloud computing.
I believe that using cloud computing is good to me.
I believe that using cloud computing is appropriate for me.
[6],
[3],
[7].
The trust in
cloud service
provider
(TT)
The cloud service provider guarantees the anonymity of users.
The cloud service provider ensures the security of my personal
data.
The cloud service provider is efficient and always works
reliably.
The cloud service provider is predictable and unchanging.
I can rely on the cloud service provider.
[8].

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Behavioral
intention
(BI)
You intend to use cloud computing in next three months.
You anticipate continuing to use cloud computing in the
future.
[5],
[7].
Cloud
Computing
Usage
(US)
On average, each week you use your cloud account often.
Every morning, you check your cloud account [3],
[7].
Table 2. Cronbach's Alpha Reliability of Constructs
Construct Number of
Items
Cronbach's
Alpha
Self-Efficacy
(SE)
4 .969
Perceived
usefulness
(PU)
6 .962
Perceived
Ease of Use
(EU)
3 .945
Attitude
(AT)
3 .957
Peer Influences (PI) 1 .912
Subjective Norm (SN) 3 .964
The trust in cloud service
provider (TT)
5 .997
Behavioural intention
(BI)
2 .958
Cloud Computing Usage
(US)
2 .942
Overall alpha value 29 .955
Construct validity was verified by employing factor analysis to evaluate a principal component
analysis with a varimax rotation. This approach was used to assess the convergent and
discriminant validity of items. Convergent validity was evaluated by assessing whether or not
items from a variable converged on a single construct [67] and whether or not the factor loading
for each item was more than 0.45, as recommended by Comrey and Lee [68]. According to
Comrey and Lee [68], loadings more than 0.45 may be deemed reasonable, while loadings greater
than 0.55 could be rated good, 0.63 very good, and 0.71 exceptional. Examining the cross-
loading of items on various criteria indicated the discriminant validity. Table 3 shows that there is
no evidence of weak loading.

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Table 3. Construct-Based Item Factor Analysis
Items
(-1-) (-2-) (-3-) (-4-) (-5-) Its evaluation
SE1 .488 .314 .141 .307 .677 Very good > 0.63
SE2 .479 .525 .263 .224 .549 Fair > 0.45
SE3 .467 .312 .223 .340 .659 Very good > 0.63
SE4 .781 .470 .372 .065 .068 Excellent > 0.71
PU1 .543 .584 -.234 .355 .041 Good > 0.55
PU2 .580 .684 .327 -.015 .229 Very good > 0.63
PU3 .621 .532 -.292 .341 .163 Very good > 0.63
PU4 .675 .432 -.123 .451 .175 Very good > 0.63
PU5 .596 .551 -.194 .314 .189 Good > 0.55
PU6 .580 .684 .327 -.015 .229 Very good > 0.63
EU1 .736 .387 -.257 .385 .072 Excellent > 0.71
EU2 .467 .805 .257 -.034 -.131 Excellent > 0.71
EU3 .495 .752 .300 .132 .188 Excellent > 0.71
AT1 .678 .540 -.131 .238 .123 Very good > 0.63
AT2 .642 .460 -.161 .516 .047 Very good > 0.63
AT3 .560 .558 -.114 .412 .054 Good > 0.55
PI1 .499 .752 .291 .082 .226 Excellent > 0.71
SN1 .177 .139 .867 .223 .280 Excellent > 0.71
SN2 .173 .116 .746 .136 .554 Excellent > 0.71
SN3 .063 .131 .719 .181 .557 Excellent > 0.71
TT1 .270 .889 -.163 .140 .155 Excellent > 0.71
TT2 -.198 -.148 .835 -.213 -.143 Excellent > 0.71
TT3 .283 .897 -.171 .034 .182 Excellent > 0.71
TT4 -.289 -.207 .232 -.295 .799 Excellent > 0.71
TT5 .736 .387 -.257 .385 .072 Excellent > 0.71
BI1 .768 .478 -.152 .312 -.020 Excellent > 0.71
BI2 .758 .488 .392 .118 .056 Excellent > 0.71
US1 .630 .514 .355 .471 .020 Very good > 0.63
US1 .735 .527 -.135 .281 -.088 Excellent > 0.71
5.2. Hypotheses Testing
A theoretical model is proposed and developed in this study by adopting and extending TPB that
lends itself to investigating trust, peer influences, perceived ease of use, and perceived usefulness
as drivers of cloud adoption in Saudi Arabia (see Figure 2). The study’s model was formulated
through the testing of eight hypotheses. Pearson's correlation analysis was used to correlate all of
the research variables, as shown in Table 4. As a consequence of the substantial correlations
between the variables (p0.01), we used the regression model to test for multicollinearity by
assessing collinearity statistics such as the variance inflation factor (VIF) and tolerance. To
determine the existence of multicollinearity effects, we searched for any alerts produced by the
AMOS report that suggested a problem of multicollinearity. The results indicate no evidence of
multicollinearity. Furthermore, regression analysis was used to establish a framework for a more
thorough examination of the potential issue of multicollinearity. As per Table 5, the tolerance

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values ranged between 0.949 and 0.400. The use of variance inflation factors (VIF) is the ideal
and only known method to assess collinearity. Although a variance inflation factor (VIF) of
below or equal to 10 (i.e., a tolerance of greater than 0.1) is commonly advised [62], a variance
inflation factor (VIF) greater than 4 suggests substantial multicollinearity problems in this
investigation. However, because the VIF values in the model ranged from 1.054 to 2.502, as
shown in Table 5, there were no VIF values greater than 4. As a result, no evidence of
multicollinearity was found.
Table 4. Variables correlation analysis
US BI AT PU EU TT SN SE
BI .747*
AT .785* .749*
PU .684* .566* .687*
EU .679* .638* .729* .740*
TT .600* .631* .720* .735* .620*
SN .559* .513* .531* .419* .325* .331*
SE .750* .658* .685* .566* .546* .526* .352*
PI .556* .678* .584* .490* .384* .429* .446* .502*
US: Usage, BI: Behavioural intention, AT: Attitude, PU: Perceived Usefulness, EU Perceived Ease of Use,
TT: trust in cloud service provider, SN: Subjective Norm, SE: Self-Efficacy, PI: Peer Influences.
* p ≤ 0.01
Table 5. Multicollinearity examination
Dependent
variable
Path
direction
Independent
variables
(predictors)
Collinearity Statistics
Tolerance VIF
Usage  Intention .400 2.502
Intention  Attitude (AT) .435 2.300
Intention

Subjective Norm
(SN)
.718 1.392
Intention  Self-efficacy (SE) .530 1.886
Attitude (AT)

The trust in cloud
service provider
(TT)
.457 2.188
Attitude (AT)  Perceived
Usefulness (PU) .661 1.513
Attitude (AT)  Perceived Ease of
Use (EU)
.619 1.616
Subjective Norm
(SN)
 Peer Influences
(PI)
.949 1.054
Multiple regression analysis was applied to evaluate the study's hypotheses after confirming that
all relevant requirements had been satisfactorily met.
"Intention" and "Usage" were engaged in the initial regression. As seen in Fig. 2, "Intention"
(0.747, Standardized path coefficient, p 0.05) is strongly and positively associated to "Usage"
(adjusted R²=0.56) (see Tables 6, 7, and 2). As a result, H8 is supported.
A regression analysis was then performed on "Behavioural Intention" using the three independent
variables "Self-Efficacy," "Attitude," and "Subjective Norm." According to the results, which are

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depicted in Fig. 2, there is a significant relationship between "Behavioural Intention" and all three
variables (adjusted R²=0.619): "Attitude" (= 0.749, p<0.05 Standardized path coefficient),
"Subjective Norm" (= 0.513, p<0.05 Standardized path coefficient), and "Self-Efficacy" (= 0.658,
p<0.05 Standardized path coefficient) (see Figure 2, Table 6 and 7). Consequently, support is
provided for H5, H6, and H7.
Then, "Attitude" was regressed on the three independent variables (i.e. "Trust in cloud computing
service provider," "Perceived Usefulness," and "Perceived Ease of Use"). As shown in Fig. 2, the
findings show that all three variables (adjusted R²=0.649)—"Trust in cloud computing service
provider" (0.720, Standardized path coefficient, p 0.05), "Perceived usefulness" (0.687,
Standardized path coefficient, p 0.05), and "Perceived Ease of Use" (0.729, Standardized path
coefficient, p 0.05)—have a significant relationship with "Attitude" H1, H2, and H3 are therefore
supported.
Table 6. Coefficients for Proposed model
Dependent
variable
Path
direc
tion
Independent
variables
(predictors)
Unstandardized
Coefficients
Standardi
zed
Coefficie
nts
Adjusted
R²
t Sig.
B Std. Error Beta
Usage  Intention .772 .027 .747 .557 28.765 .000
Intention

Attitude
(AT)
.443 .034 .472 .619 12.909 .000
Intention

Subjective
Norm (SN)
.137 .024 .165 .619 5.806 .000
Intention

Self-
efficacy
(SE)
.303 .036 .276 .619 8.348 .000
Attitude (AT)

The trust in
cloud
service
provider
(TT)
.317 .028 .399 .649 11.531 .000
Usefulness
(PU)
.088 .043 .082 .649 2.807 .000
Ease of Use
(EU)
.478 .040 .421 .649 12.085 .000
Subjective
Norm (SN)
 Peer
Influences
(PI)
.459 .042 .398 .589 10.986 .000

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Table 7. Weights of Regression Standardized
Criterion variable
Path
direction
Criterion variable
predictors
Estimate (Significance)
Usage  Intention .747 Significant
Intention  Attitude (AT) .749 Significant
Intention  Subjective Norm (SN) .513 Significant
Intention  Self-efficacy (SE) .658 Significant
Attitude (AT)

The trust in cloud service
provider (TT)
.720 Significant
Attitude (AT)  Perceived Usefulness (PU) .687 Significant
Attitude (AT)  Perceived Ease of Use
(EU)
.729 Significant
Subjective Norm (SN)  Peer Influences (PI) .446 Significant
"Peer Influences" and "Subjective Norm" were implemented in the final regression. As shown in
Fig. 2, "Peer Influences" (β=0.446, p 0.05 Standardized path coefficient) is shown to be
substantially and positively associated to "Subjective Norm" (adjusted R² = 0.589) (see Figure 2,
Table 6 and 7). As a result, H4 is supported.
Figure 2. The study model
6. DISCUSSION
One of the primary goals of this research was to identify the factors influencing cloud computing
adoption using the theoretical TPB concept. The author extended TPB to investigate drivers of
cloud adoption in Saudi Arabia by placing trust in the cloud computing service provider, peer

81
influences, perceived usefulness, and ease of use as new constructs within TPB. The study model
also investigated the factors influencing the decisions of higher education students in Saudi
Arabia to use cloud computing. The study's findings demonstrate that the proposed model
successfully explained a sizable portion of the variation in cloud adoption. All of the study
hypotheses are supported. Cloud users' attitudes toward cloud adoption were found to be
significantly influenced by perceived usefulness, perceived ease of use, and trust in cloud
computing service provider variables.
In an earlier study, the author devised a formula to approximate the contribution of each model's
factor to the explanatory power of the model [62].
Where:
= Participation of variable Ax in a model' explanatory power
= Square of beta coefficients or standardized coefficients of variable
= Model' explanatory power (y)
= Total of causal effects for the model’s constructs
The study applies the equation mentioned above to calculate the explanatory power of every
construct and its antecedents, as well as the rate at which each antecedent adds to a construct's
explanatory power. The formula was used to calculate how much the "attitude's" antecedents
contributed to its explanatory power. Table 8 summarizes the findings.
The findings demonstrate that university students' attitudes toward using cloud computing are
significantly influenced by perceived ease of use, trust in cloud computing service provider and
perceived usefulness, which have the ability to explain their attitude by 22.15%, 21.9% and
20.9% respectively.
Table 8. Attitude’s variables' participation in its explanatory power
Antecedents Attitude
Trust in cloud computing service provider
(TT)
21.9%
Perceived Usefulness (PU) 20.9%
Perceived Ease of Use (EU) 22.15%
Total 65.00%
This proposes that the perceived usefulness of cloud computing encourages university students to
use cloud computing to get their work done. Cloud computing has grown in popularity in recent
years, and it offers various potential benefits for university students. The perceived usefulness of
using cloud computing for university students includes increased accessibility, flexibility,
collaboration, and cost-effectiveness.
One of the most significant advantages of cloud computing is its accessibility. Students can
access their files and applications from any device with an internet connection, making it easier
to work on assignments and projects from anywhere. This feature also allows for greater
flexibility in terms of scheduling and location. Cloud computing also promotes collaboration
among students. With cloud-based tools such as Google Drive or Microsoft OneDrive, multiple

82
users can work on the same document simultaneously, making group projects more efficient and
effective.
By collaborating with peers and sharing knowledge about best practices for using cloud
computing platforms like Google Drive or Dropbox, students can streamline their work processes
and improve their productivity.
Moreover, knowledge sharing can also help students overcome any technical difficulties they
may encounter while using cloud computing tools. By working together and pooling their
resources, students can troubleshoot issues more efficiently and effectively than if they were
working alone [69].
Cloud computing is a technology that allows users to access and store data remotely, making it an
attractive option for students who need to collaborate on projects or access their work from
multiple devices. However, if the technology is difficult to learn and use, students may not see
the value in using it. Therefore, it is important for universities to provide training and support for
students to ensure they can effectively use cloud computing.
Additionally, the perceived usefulness of cloud computing may vary depending on the specific
needs of each student. For example, a student who primarily works alone may not see as much
benefit from using cloud computing as a student who frequently collaborates with others.
Moreover, cloud computing can be cost-effective for university students who may not have the
resources to purchase expensive software or hardware. Cloud-based services often offer
affordable subscription plans that allow students to access a variety of tools without breaking the
bank.
Overall, the perceived usefulness of using cloud computing for university students is undeniable.
Its accessibility, flexibility, collaboration features, and cost-effectiveness make it an attractive
option for modern-day learners seeking a more efficient way to study and complete academic
tasks.
While cloud computing can be a valuable tool for university students, its effectiveness depends
on how well it meets each individual's needs and how easily it can be learned.
The results illustrate that the perceived ease of use motivates university students to use cloud
computing to get their work done.
Cloud computing has become an increasingly popular technology among university students due
to its convenience and accessibility. However, the perceived ease of use of cloud computing can
have a significant impact on whether or not students choose to utilize this technology.
Perceived ease of use refers to the degree to which individuals believe that using a particular
technology will be effortless and straightforward. If students perceive cloud computing as
difficult or complicated to use, they may be less likely to adopt it for their academic needs.
On the other hand, if students perceive cloud computing as easy and user-friendly, they are more
likely to embrace it as a valuable tool for storing and accessing their coursework, collaborating
with peers, and completing assignments remotely. Therefore, it is crucial for universities to
ensure that their students receive adequate training and support in using cloud computing
technologies. By promoting the perceived ease of use of these tools among their student
population, universities can encourage greater adoption of cloud computing and enhance the
overall learning experience for their students.

83
The study's results also show that the students’ attitudes toward cloud adoption were found to be
significantly influenced by their trust in cloud computing service providers. Trust in cloud
computing service providers refers to the confidence that users have in the reliability, security,
and privacy of their data stored on cloud servers.
Reliability is a crucial factor in building trust between service providers and their clients. In the
world of cloud computing, reliability is even more important as it involves the storage and
management of sensitive data. For university students, cloud computing has become an essential
tool for accessing course materials, collaborating with peers, and submitting assignments.
Service providers must ensure that their systems are reliable to maintain the trust of their clients.
This means having robust security measures in place to protect against cyber threats and ensuring
that data is backed up regularly to prevent loss. Service providers should also have a clear
communication plan in place to inform clients of any potential disruptions or downtime.
Students must also be confident that their data is secure and safe from unauthorized access or
theft. If they do not trust the service provider's ability to safeguard their data, they may be
hesitant to adopt cloud computing solutions. To build trust in cloud computing service providers,
universities should ensure that they partner with reputable and trustworthy companies with a
proven track record of providing secure and reliable services. Additionally, universities should
educate their students about best practices for securing their data when using cloud services. It is
essential for universities to ensure that their students' data is safe and secure while using these
services by partnering with trustworthy companies and educating them about best practices for
securing their data.
Cloud service providers must ensure that they have robust security measures in place to protect
student data from unauthorized access. They should also be transparent about how they collect,
store, and use student data. This transparency builds trust and reassures students that their
information is safe. When students trust cloud service providers, they are more likely to adopt
cloud computing as a means of storing and accessing their data. This adoption can lead to
increased productivity and collaboration among students. Thus, cloud service providers must
prioritize security and transparency to build trust among users. By doing so, they can help unlock
the full potential of cloud computing for educational purposes.
Once more, the Al-ghaith equation [62] was applied to determine the contribution of the students
―intention" antecedents to its explaining ability, and the findings are outlined in Table 9. The
findings demonstrates that "subjective norm" alone accounts for 33.95% of students' "intentions"
regarding adoption of cloud computing, followed by "attitude," which explains roughly 14.24%
of "behavioral intentions," and finally "self-efficacy," which explains 13.71%.
Table 9. Behavioural Intention variables' participation in its explanatory power
Antecedents Behavioural Intention
Attitude (AT) 14.24%
Subjective Norm (SN) 33.95%
Self-efficacy (SE) 13.71%
Total 61.90%
The study has shown that subjective norms play an important role in shaping students' attitudes
and intentions towards using cloud computing. Students who perceive high levels of social
pressure to adopt these technologies are more likely to view them positively and intend to use
them in their academic work.

84
The subjective norm refers to the perceived social pressure that individuals feel to conform to
certain behaviors or beliefs. In the context of cloud computing, this can refer to the influence that
peers or other members of a student's social network have on their decision to use cloud-based
technologies.
Peer influences plays a crucial role in shaping the subjective norms and behaviors of university
students towards cloud computing. Peer influence refers to the impact that peers have on an
individual's behavior.
In the context of cloud computing, peer influence can play a significant role in shaping students'
behavior towards this technology. If their peers are using cloud computing and find it beneficial,
they may be more likely to adopt it themselves. Similarly, if their peers have negative
experiences with cloud computing, they may be less likely to use it. Students tend to follow their
peers' behavior when it comes to adopting new technologies such as cloud computing. The
perception that using cloud computing is socially acceptable among peers can significantly
impact their decision-making process.
Overall, peer influence and subjective norms are important factors that should be considered
when designing strategies to promote the adoption of cloud computing among university
students. By understanding these factors and addressing them appropriately, cloud service
providers can increase the likelihood that university’ students will embrace this technology and
reap its benefits. They can leverage peer influences by encouraging early adopters to share their
positive experiences with others.
Self-efficacy is the second crucial factor in determining an individual's intention to use cloud
computing. In the context of university students, self-efficacy can be defined as the belief in one's
ability to effectively use cloud computing technology for academic purposes. University students'
self-efficacy towards using cloud computing can be influenced by various factors such as their
prior experience, knowledge, and skills. The use of cloud computing has become increasingly
popular among university students due to its convenience and accessibility. However, some
students may lack the necessary skills or knowledge to use it effectively. This can lead to a
decrease in their self-efficacy towards using cloud computing.
To improve self-efficacy, universities can provide training programs or workshops that focus on
developing the necessary skills and knowledge required for effective use of cloud computing.
Additionally, providing access to resources such as online tutorials or support services can also
help increase self-efficacy. Thus, universities should focus on enhancing students' self-efficacy
by providing adequate training, resources, and support for using cloud computing technology.
This will not only increase their confidence but also improve their academic performance by
enabling them to access resources more efficiently. Therefore, it is important for universities to
recognize the significance of self-efficacy in promoting the adoption of new technologies such as
cloud computing among their students.
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88
AUTHOR
Waleed A. Alghaith is an Associate Professor of Information systems at Imam
Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia. His areas of
expertise are information technology research, internet research, technology adoption, data
science, sentiment analysis, cloud computing, artificial intelligence and organizational
intelligence technologies. Email: waalghaith@imamu.edu.sa

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